Simplified Aeroelastic Model for Fluid Structure Interaction between Microcantilever Sensors and Fluid Surroundings

Fluid-structural coupling occurs when microcantilever sensors vibrate in a fluid. Due to the complexity of the mechanical characteristics of microcantilevers and lack of high-precision microscopic mechanical testing instruments, effective methods for studying the fluid-structural coupling of microcantilevers are lacking, especially for non-rectangular microcantilevers. Here, we report fluid-structure interactions (FSI) of the cable-membrane structure via a macroscopic study. The simplified aeroelastic model was introduced into the microscopic field to establish a fluid-structure coupling vibration model for microcantilever sensors. We used the finite element method to solve the coupled FSI system. Based on the simplified aeroelastic model, simulation analysis of the effects of the air environment on the vibration of the commonly used rectangular microcantilever was also performed. The obtained results are consistent with the literature. The proposed model can also be applied to the auxiliary design of rectangular and non-rectangular sensors used in fluid environments.     

气候变化下中国八种梧桐属树种潜在适生区模拟

中国梧桐属（Firmiana）在世界梧桐属中占比较大，且除梧桐外其余种均为中国特有且分布范围狭窄的植物种，灭绝风险大，研究气候变化对中国梧桐属树种的影响对于维护生物多样性具有重要的意义。结合多时期第六次国际气候耦合模式比较计划（CMIP6）气候变量数据和中国八种梧桐属树种的分布数据，基于R语言kuenm程序包优化的最大熵（Maxent）模型模拟分析中国八种梧桐属树种在多尺度下的潜在适生区，得出梧桐属最适宜的模拟尺度、潜在适生区的面积变化和迁移方向、梧桐属多样性保护关键区域及保护空缺。结果表明：（1）梧桐属最适宜的模拟尺度为亚洲；（2） Maxent模型的接收者操作特征曲线下面积（AUC）值均大于0.9，表明模型对梧桐属潜在适生区预测结果具有较高准确度；（3）气候变化影响下除云南梧桐（Firmiana major）外其它树种的潜在适生区都将在未来有所扩大；（4）中国八种梧桐属树种潜在适生区迁移方向主要为东西向，南北向大跨度迁移较少，纬度变化不大；（5）丹霞梧桐（Firmiana danxiaensis）的稳定潜在适生区最小；（6）中国梧桐属多样性保护关键区域主要分布于广西壮族自治区及云南、广东、海南等省区；（7）中国梧桐属多样性保护空缺区域主要分布于广西壮族自治区中部及海南省北部；（8）梧桐属多样性保护关键区域正在为人造地表所侵蚀。研究分析气候变化对中国八种梧桐属树种的影响及其潜在适生区变化、中国梧桐属多样性保护状态，可为中国梧桐属建立多样性保护廊道提供相关建议，为制定多样性保护规划及相应措施提供参考。     

Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755–0.771) to 0.769 (95% CI: 0.761–0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.